Wayfair sells over 10 million products on our website. This vast selection ensures that customers have numerous options when shopping for a particular item; but it also makes effective, personalized product recommendations of vital importance in helping our customers find products that are relevant to their interests. This week in……

Product tags reveals the detailed characteristics of a product which can be used to power PDP (product display page) creation, searching, filter creation and more.This week in Wayfair Data Science’s explainer series, Senior Data Scientist Jinnie Chen outlines some of the strategies we applied in Wayfair to perform automated product……

Serving effective personalized product recommendations is critical to providing a pleasant shopping experience for customers at Wayfair. To do this, the Wayfair Data Science team builds state of the art recommender systems that leverage the customer’s previous browsing history to surface products that match their interests. This week in Wayfair……

Most machine learning algorithms are designed to train on balanced datasets. Resultantly, when our data are highly imbalanced, a typical model will have atrocious recall. In this video, Wayfair Senior Data Scientist Trent Woodbury explains the three most common ways of handling this imbalanced data problem. Trent Woodbury……

This week in Wayfair Data Science’s Explainer Series, Data Science Tech Lead Peter B. Golbus discusses machine learning from a theoretical computer science perspective. In this video, we describe multiclass classification as an encoding task, i.e. a process for building compression schemes that convert large “files” (feature vectors) into small……

This week in Wayfair Data Science’s explainer series, we’re discussing object pose estimation, an important problem in robotics and augmented reality (AR) applications. In robotics, when given a 3D model of an object a mobile robot must be able to localize it in space in order to manipulate it. This……

Wayfair has a strong emphasis on causal inference when evaluating the impact of business strategies. The assumptions of experimentation and regression are critical to know your models are capturing true effects and not confounding variables. In this video, Wayfair data scientist Dan VanLunen outlines some of these key assumptions along……

This week in Wayfair Data Science’s explainer series, Senior Machine Learning Engineer Tim Zhang lays out what you need to know about training image synthesis. Training image synthesis is a fairly young project for the Computer Vision team at Wayfair. We have explored a few different use cases that can……

This week in Wayfair Data Science’s explainer series, Tim O’Connor discusses experimentation in the context of data science. Experiments are crucial to data science, helping to determine which version of a model is to use in future iterations of a system or generating new sources of data unavailable in a……

Welcome back to Wayfair Data Science’s Explainer Series! This week, Afshaan Mazagonwalla will be speaking about Bayesian Machine Learning Wayfair uses Bayesian and Reinforcement Learning based techniques for balancing the exploration – exploitation trade off between new and popular items in ranking products on the website and for recommending sales……

Find out about how Wayfair tackles product recommendations in our first installment! Wayfair Data Science is composed of a number of sub-teams, each tackling a different set of specific business challenges. In order to give you a taste of the wide array of people and workstreams we have here……